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Soofi-S-Rhine-Preview generates text responses with explicit chain-of-thought reasoning before the final answer. It is one of two reasoning ("thinking") variants of SOOFI-S, a sovereign, open-source language model developed by a German research consortium, alongside Soofi-S-Isar-Preview. SOOFI (Sovereign Open Source Foundation Models) is designed to provide a secure, European open-source alternative to US and Chinese AI models for industrial use, featuring strong reasoning and AI agent capabilities.
TODO: describe how the Rhine reasoning variant differs from Isar (e.g. training data, reasoning style, target tasks).
This model is for research and development only (Preview).
Released under a custom license ("Other"). TODO: add the full license text / link — the official card references a License section that is not yet filled in.
Global (open release on the Hugging Face Hub). Development and training infrastructure are located in Europe (see Computational Load).
Enterprise developers and researchers seeking a sovereign, European open-source LLM for industrial use: tasks that benefit from explicit step-by-step reasoning (math, logic, planning, complex analysis) and AI-agent / tool-use workflows. English and German are the primary languages.
Hugging Face Hub — Preview at https://huggingface.co/Soofi-Project/Soofi-S-Rhine-Preview. TODO: final release date (MM/DD/YYYY).
Architecture Type: Transformer-based hybrid Mixture-of-Experts (MoE) with Mamba-2 state-space (SSM) layers and attention layers. Network Architecture: Custom Hybrid Mamba-2/MoE (Nemotron-style), designed from scratch — 23 Mamba-2/MoE layers + 6 attention layers; 128 routing experts
This model was developed from scratch (no base model). Number of model parameters: 3.0×10^10 total (30B), with ~3.5B active parameters during inference.
Cumulative Compute: TODO. Estimated Energy and Emissions for Model Training: TODO. Training infrastructure is hosted entirely in Europe on T-Systems' Industrial AI Cloud (Deutsche Telekom) to ensure data sovereignty.
Input Type(s): Text
Input Format(s): String
Input Parameters: One-Dimensional (1D)
Other Properties Related to Input: Chat/ChatML-style messages via the
embedded chat template. No system prompt is required (none is injected by
default). Context length: see config.json (TODO: confirm maximum context).
Output Type(s): Text
Output Format(s): String
Output Parameters: One-Dimensional (1D)
Other Properties Related to Output: As a reasoning model, Rhine emits explicit
thinking traces (a <think> block) before the final answer; allow a generous
max_new_tokens budget. Supports the model's native tool-calling format.
Runtime Engine(s):
transformers (trust_remote_code=True) Supported Hardware Microarchitecture Compatibility:
Preferred/Supported Operating System(s):
The integration of foundation and fine-tuned models into AI systems requires additional testing using use-case-specific data to ensure safe and effective deployment.
…-GGUF (llama.cpp/Ollama) and …-FP8 (vLLM); see
Related models.SOOFI-S ships with custom modeling code. You must load it using trust_remote_code=True with transformers.
python
from transformers import AutoModelForCausalLM, AutoTokenizermodel_id = "Soofi-Project/Soofi-S-Rhine-Preview"tok = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)model = AutoModelForCausalLM.from_pretrained(model_id, trust_remote_code=True, torch_dtype="auto", device_map="auto")# No system prompt is required (none is injected by default).messages = [{"role": "user", "content": "How many r's are in strawberry?"}]inputs = tok.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)out = model.generate(**{"input_ids": inputs}) # sampling defaults come from generation_config.jsonprint(tok.decode(out[0][inputs.shape[-1]:], skip_special_tokens=True))
As a reasoning model, Rhine emits explicit thinking traces before the final answer; allow a generous
max_new_tokensbudget.
Link: TODO. Data Modality: Text. Text Training Data Size: More than 10 Trillion Tokens (~25T). Data Collection Method by dataset: Hybrid (freely available, high-quality sources). TODO: refine. Labeling Method by dataset: TODO. Properties: Trained entirely from scratch on freely available, high-quality tokens.
Link: TODO. Properties: TODO.
Link: TODO. Benchmark Score: TODO — add key reasoning benchmarks once available. Properties: TODO.
Acceleration Engine: transformers; vLLM / llama.cpp via quantized
variants.
Specific Test Hardware: TODO.
The SOOFI consortium believes Trustworthy AI is a shared responsibility and has established policies and practices to enable development for a wide array of AI applications. When downloaded or used, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse.
For more detailed information, see the Model Card++ subcards below. Please report model quality, risk, security vulnerabilities, or concerns to contact@soofi.info.
| Field | Response |
|---|---|
| Participation considerations from adversely impacted groups in model design and testing | TODO |
| Measures taken to mitigate against unwanted bias | TODO |
| Bias Metric (if measured) | TODO |
| Field | Response |
|---|---|
| Intended Task/Domain | Reasoning-heavy tasks (math, logic, planning, analysis), AI-agent/tool use |
| Model Type | Hybrid Mixture-of-Experts (MoE) autoregressive reasoning ("thinking") language model |
| Intended Users | Enterprise developers and researchers |
| Output | Text (String), with an explicit <think> reasoning trace before the answer |
| Describe how the model works | Generates text autoregressively; a router activates 6 of 128 experts per token across hybrid Mamba-2/MoE and attention layers; emits chain-of-thought before the final answer |
| Technical Limitations | Preview checkpoint; non-primary languages (FR/IT/ES) are limited; reasoning traces can be verbose; may produce inaccurate or outdated content (knowledge cutoff end of 2025) |
| Verified to have met prescribed quality standards |
| Field | Response |
|---|---|
| Generatable or reverse engineerable personal data? | TODO |
| Personal data used to create this model? | TODO |
| Was consent obtained for any personal data used? | TODO |
| How often is dataset reviewed? | TODO |
| Was data from user interactions with the AI model used to train the model? | No |
| Is there provenance for all datasets used in training? | TODO |
| Applicable Privacy Policy | TODO |
| Field | Response |
|---|---|
| Model Application Field(s) | Industrial use; customer service; reasoning, planning, and agent applications |
| Describe the life critical impact (if present) | None intended. Not for use in life-critical or safety-critical decision-making without independent validation |
| Use Case Restrictions | Abide by the applicable license agreement (see License/Terms of Use) |
| Model and dataset restrictions | TODO |
bibtex
@misc{soofi_s_rhine_preview,title = {Soofi-S-Rhine-Preview},author = {SOOFI Consortium},year = {2026},url = {https://huggingface.co/Soofi-Project/Soofi-S-Rhine-Preview}}
| TODO |
| Performance Metrics | TODO (see Evaluation Dataset) |
| Potential Known Risks and Mitigation | May generate incorrect, biased, or unsafe content, including flawed reasoning; apply use-case-specific testing and guardrails before deployment |
| Terms of Use/Licensing | Other (see License/Terms of Use) |